""" Boiler plate
- import all packages
- emulate snakemake so can load config as usual
- load the network you want
TODO: make it easier to restore old runs
TODO: hide all the boiler pates in a file
"""
%load_ext autoreload
%autoreload 2
%precision %e
import logging
import pypsa
import os.path

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import pandas as pd
# running the jupyter notebook on the compute nodes doesnt build the path as expected, you have to manually do this
import IPython

working_directory = os.path.dirname(IPython.extract_module_locals()[1]['__vsc_ipynb_file__'])
workflow_dir = os.path.dirname(working_directory)
scripts_dir = os.path.join(workflow_dir, "scripts")
root_dir = os.path.dirname(workflow_dir)

os.chdir(scripts_dir)


logging.basicConfig(level=logging.CRITICAL)
logger = logging.getLogger(__name__)


from _helpers import configure_logging, mock_snakemake
# from make_summary import assign_carriers
import _plot_utilities
import plot_network 
logging.getLogger('plot_network').setLevel(logging.CRITICAL)
logging.getLogger('_plot_utilities').setLevel(logging.CRITICAL)

from constants import PLOT_COST_UNITS, PLOT_CAP_UNITS,PLOT_SUPPLY_UNITS
from _plot_utilities import fix_network_names_colors, determine_plottable
from plot_network import plot_cost_map, plot_map

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

REMIND_COUPLED = True
PLANNING_YEAR = 2035
config = None if not REMIND_COUPLED else "resources/tmp/remind_coupled.yaml"

snakemake = mock_snakemake(
    "plot_network",
    snakefile_path=workflow_dir,
    topology="current+FCG",
    # co2_pathway="exp175default",
    co2_pathway="SSP2-PkBudg1000-PyPS",
    planning_horizons=PLANNING_YEAR,
    configfiles = config,
    heating_demand="positive",
)

configure_logging(snakemake, logger=logger)
config = snakemake.config
tech_colors = config["plotting"]["tech_colors"]


ntw_path = snakemake.input.network
# ntw_path = f"/home/ivanra/downloads/PaperResultsXiaowei_networks/postnetwork-ll-current+Neighbor-exponential175-{PLANNING_YEAR}.nc"
# ntw_path = "/p/tmp/ivanra/xiaowei-pypsa/PyPSA-China/results/version-0325.175.1H/postnetworks/positive/postnetwork-ll-current+Neighbor-exponential175-2060.nc"
n = pypsa.Network(ntw_path)
results_dir = os.path.dirname(os.path.dirname(ntw_path))
 

2025-06-06 08:55:47,990 - _helpers.py - INFO - =========== NEW RUN ===========
2025-06-06 08:55:47,990 - _helpers.py - INFO - =========== NEW RUN ===========
INFO:__main__:=========== NEW RUN ===========

Fix network for plotting // add missing info

fix_network_names_colors(n, config)
determine_plottable(n)

/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/_plot_utilities.py:232: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  n.carriers.color.fillna(NAN_COLOR, inplace=True)

Plot maps

plot capex

ax = plot_cost_map(n, config["plotting"], cost_pannel=True, capex_only=True)

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '
/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:190: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:349: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()

# TODO fix layout
ax = plot_cost_map(n, config["plotting"], cost_pannel=True, plot_additions=False, capex_only=False)

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:190: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:349: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()

plot electricy generation map

from plot_network import plot_energy_map
from _plot_utilities import set_plot_style
set_plot_style(
    style_config_file= os.path.abspath("../../config/plotting_styles/network_map.mplstyle"),
    base_styles=["classic", "seaborn-v0_8-white"],
)
plot_energy_map(n,config["plotting"], carrier="AC", components = ["Generator", "Link"])

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:504: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/plot_network.py:465: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  fig.tight_layout()

from plot_network import plot_energy_map
from _plot_utilities import set_plot_style

if snakemake.config["heat_coupling"]:
    set_plot_style(
        style_config_file= os.path.abspath("../../config/plotting_styles/network_map.mplstyle"),
        #snakemake.config["plotting"]["network_style_config_file"],
        base_styles=["classic", "seaborn-v0_8-white"],
    )
    plot_energy_map(n,config["plotting"], carrier="heat", components = ["Generator", "Link"])

Nodal prices

from plot_network import plot_nodal_prices
plot_nodal_prices(n, config["plotting"], "AC")

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '

Interactive plot map

import numpy as np
# make names and link sizes, make sure we only plot AC, DC & statiosn
ac_links = n.links[n.links.carrier == "AC"]
colors = n.links.index.to_series().apply(lambda x: 'black' if 'ext' in x else 'pink')
widths = np.log(n.links.p_nom_opt + 3) / 2
widths[~widths.index.isin(ac_links.index)] = 0
widths[widths.index.str.contains('reversed')] = 0
names = n.links.copy()
names["name"] = names.index.values
names.loc[~names.index.isin(ac_links.index), "p_nom_opt"] = ""
names.loc[~names.index.isin(ac_links.index), "name"] = ""
buses = n.buses.copy()
buses["name"] = buses.apply(lambda x: f"{x.name}" if x.carrier == "AC" or x.carrier=="stations" else "", axis=1)
buses["sizes"] = buses.apply(lambda x:10 if x.carrier == "AC" or x.carrier=="stations" else 0, axis=1)
buses["colors"] = buses.apply(lambda x: "black" if x.carrier == "AC" else "red", axis=1)
buses.carrier.unique()
ax = n.iplot(link_colors=colors, link_widths=widths, link_text = names.p_nom_opt.astype(str) + names.name, bus_text=buses.name, bus_sizes=buses.sizes, bus_colors=buses.colors)

/p/tmp/ivanra/anaconda/ipykernel_2927777/316105652.py:10: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  names.loc[~names.index.isin(ac_links.index), "p_nom_opt"] = ""

extendable lines

fig, ax = plt.subplots(1, 1, figsize=(10, 10),subplot_kw={"projection":ccrs.PlateCarree()})
def width(x):
    return np.log10(x/1e6).where(x>1,0)
n.plot(ax = ax,link_colors=n.links.p_nom_extendable.apply(lambda x: "red" if not x else "black"),line_colors=n.lines.s_nom_extendable.apply(lambda x: "red" if not x else "black"), link_widths= width(n.links.p_nom_opt) , line_widths=width(n.lines.s_nom_opt), link_alpha=0.5)
# Create custom legend
import matplotlib.patches as mpatches
extendable_patch = mpatches.Patch(color='black', label='Extendable')
non_extendable_patch = mpatches.Patch(color='red', label='Fixed')
ax.legend(handles=[extendable_patch, non_extendable_patch], title = "Links/Lines log width")

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/cartopy/mpl/feature_artist.py:144: UserWarning: facecolor will have no effect as it has been defined as "never".
  warnings.warn('facecolor will have no effect as it has been '

<matplotlib.legend.Legend at 0x7fbbea642840>

Shadow prices & prices

from make_summary import make_summaries
summaries = make_summaries({("exp175-paper", PLANNING_YEAR):ntw_path})

/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:525: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.generators.loc[gens, "bus"], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:556: RuntimeWarning: invalid value encountered in scalar divide
  market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)
/p/tmp/ivanra/PyPSA-China-PIK/workflow/scripts/make_summary.py:549: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.
  .groupby(n.links.loc[links, "bus" + i], axis=1)

CO2 emissions & Stores

n.global_constraints

attribute	type	investment_period	carrier_attribute	sense	constant	mu
GlobalConstraint

fig, ax = plt.subplots()
# calc the t resolved CO2 emissions from generators
time_res_emissions = ((n.generators_t.p/ n.generators.efficiency)* n.generators.carrier.map(n.carriers.co2_emissions)).T.groupby(n.generators.carrier).sum()
colors = time_res_emissions.T.columns.map(tech_colors).fillna("lightgrey")
time_res_emissions.T.plot(ax =ax, color =  colors, legend = True)

<Axes: xlabel='snapshot'>

gen_emissions = ((n.generators_t.p/ n.generators.efficiency)* n.generators.carrier.map(n.carriers.co2_emissions)).T.groupby(n.generators.carrier).sum().T.cumsum()
fig, ax = plt.subplots()
gen_emissions.where(gen_emissions>=0).dropna(axis=1, how="all").plot(ax=ax)
ax.set_ylabel("cum CO2 emissions [t]")

Text(0, 0.5, 'cum CO2 emissions [t]')

try:
    fig, ax = plt.subplots()
    # calc the t resolved CO2 emissions from generators
    ((n.generators_t.p/ n.generators.efficiency)* n.generators.carrier.map(n.carriers.co2_emissions)).T.groupby(n.generators.carrier).sum().sum().T.cumsum().plot(ax =ax, label = "CO2 emissions (cum)", lw=4, c ="black", ls = "--")
    n.stores_t.e.T.groupby(n.stores.carrier).sum().loc[["CO2","H2", "CO2 capture", "gas", "biomass"]].T.plot(lw=4, cmap ="jet", ax =ax)
    ax.legend()
    ax.semilogy()
    ax.set_ylim(1e3,1e10)
    ax.set_ylabel("carrier stock")
except KeyError:
    print("Overnight model does not have CO2 capture or biomass stores, skipped")

Overnight model does not have CO2 capture or biomass stores, skipped

gas_stores = n.stores[n.stores.carrier.str.contains("gas")]
gas_stores_t = n.stores_t.e[n.stores_t.e.columns.intersection(gas_stores.index)]
gas_stores_t.sum(axis=1).plot()

<Axes: xlabel='snapshot'>

CO2 capture

! beware the store is the difference :)

try:
    stores = n.stores_t.e.T.groupby(n.stores.carrier).sum()
    diff = stores.iloc[:, -1] -stores.iloc[:, 0]
    co2_cap = stores.iloc[:, -1].loc[["CO2 capture"]].sum()
    co2_cap
except KeyError:
    print("Overnight model does not have CO2 capture or biomass stores, skipped")

Overnight model does not have CO2 capture or biomass stores, skipped

n.stores_t.e.T.groupby(n.stores.carrier).sum().T.plot(lw=4, cmap ="jet")

<Axes: xlabel='snapshot'>

Time series

Weekly energy balances

from plot_time_series import plot_energy_balance

ax = plot_energy_balance(n, config["plotting"], start_date=f"{PLANNING_YEAR}-01-01 01:00", end_date=f"{PLANNING_YEAR}-12-31 12:00:00")
ax.grid(axis='y')
ax.set_title("Electricity Balance")

Text(0.5, 1.0, 'Electricity Balance')

ax = plot_energy_balance(n, config["plotting"], start_date=f"{PLANNING_YEAR}-07-24 00:00", end_date=f"{PLANNING_YEAR}-07-31 12:00:00")
ax.grid(axis='y')
ax.set_title("Electricity Balance")

Text(0.5, 1.0, 'Electricity Balance')

ax = plot_energy_balance(n, config["plotting"], start_date=f"{PLANNING_YEAR}-03-31 21:00", end_date=f"{PLANNING_YEAR}-04-06 12:00:00")
ax.grid(axis='y')
ax.set_title("Electricity Balance")

Text(0.5, 1.0, 'Electricity Balance')

price series

from constants import PROV_NAMES 
marginal_price_series = n.buses_t["marginal_price"][PROV_NAMES]
prov_max_price_series = marginal_price_series.T.max()
prov_min_price_series = marginal_price_series.T.min()
spread = marginal_price_series.T.max()-marginal_price_series.T.min()
load_ac = n.statistics.withdrawal(bus_carrier="AC", aggregate_time=False, groupby="location", comps = "Load")

regional_weighed_price = marginal_price_series.T.mul(load_ac).T.sum()/load_ac.T.sum()
order =regional_weighed_price.sort_values().index

weighed_prices = (marginal_price_series.T.mul(load_ac)/load_ac.sum()).sum()
ax = marginal_price_series[order].plot(alpha=0.5, lw=2, legend=False, cmap="plasma")
weighed_prices.plot(ax = ax, lw=3, c="black", label = "weighted price avg", alpha =0.8, ls = "--")
ax.legend(  loc='upper left', bbox_to_anchor=(1, 1), ncols=2)

<matplotlib.legend.Legend at 0x7fbbdb6d74a0>

from _plot_utilities import find_weeks_of_interest
from constants import PROV_NAMES

winter_week, summer_week = find_weeks_of_interest(n, f"{PLANNING_YEAR}-04-01", f"{PLANNING_YEAR}-10-06")
marginal_price_series = n.buses_t["marginal_price"][PROV_NAMES]
prov_max_price_series = marginal_price_series.T.max()
prov_min_price_series = marginal_price_series.T.min()
spread = marginal_price_series.T.max()-marginal_price_series.T.min()
load_ac = n.statistics.withdrawal(bus_carrier="AC", aggregate_time=False, groupby="location", comps = "Load")
weighed_prices = (marginal_price_series.T.mul(load_ac)/load_ac.sum()).sum()
print("calculated prices")

ax = prov_min_price_series.loc[summer_week].plot(label = "Marginal price\n(least expensive province)")
prov_max_price_series.loc[summer_week].plot(label = "Marginal price\n(most expensive province)", ax =ax)
# spread.loc[summer_week].plot(label = "Price spread", ax =ax, ls= "--")
ax.set_ylabel("Price [€/MWh]")
# ax.vlines(winter_week[len(winter_week)//2], 0,prov_max_price_series.max()*1.2, color="black", ls = "--", label="peak event")
ax.vlines(summer_week[len(summer_week)//2], 0,prov_max_price_series.loc[summer_week].max()*1.2, color="black", ls ="--", label="peak event")

weighed_prices.loc[summer_week].plot(ax=ax, label ="national mean weighted price")
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title="Legend")

calculated prices

<matplotlib.legend.Legend at 0x7fbbd9c74e30>

ax = prov_min_price_series.plot(label = "Marginal price\n(least expensive province)", logy=True)
ax = prov_max_price_series.plot(label = "Marginal price\n(most expensive province)", logy=True)
ax.set_ylabel("Price [€/MWh]")
ax.vlines(winter_week[len(winter_week)//2], 0,prov_max_price_series.max()*1.2, color="black", ls = "--", label="peak event")
ax.vlines(summer_week[len(summer_week)//2], 0,prov_max_price_series.max()*1.2, color="black", ls ="--", label="peak event")

weighed_prices.plot(ax=ax, label ="national mean weighted price", logy=True)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title="Legend")

<matplotlib.legend.Legend at 0x7fbbd8719df0>

Prices - balance overlay

ax = plot_energy_balance(n, config["plotting"], start_date=str(winter_week[0]), end_date=str(winter_week[-1]))
ax.grid(axis='y')
ax.set_title("Electricity Balance - worst week winter")
ax2 = ax.twinx()
prov_max_price_series.loc[winter_week].plot(ax=ax2, color="red", lw=2, ls = "--", label= "Most exp Prov")
weighed_prices.loc[winter_week].plot(ax=ax2, color = "blue", lw=3, ls = ":",label = "National weighted price")
fig = ax.get_figure()
ax2.set_ylabel("PriceEur/MWh", color="red")
ax2.tick_params(axis='y', labelcolor='red')
# Move the existing legend to the right
handles, labels = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
ax.legend(handles + handles2, labels + labels2, loc='center left', bbox_to_anchor=(1.1, 0.5))

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:1798: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.
  ax.set_ylim(0, None)

<matplotlib.legend.Legend at 0x7fbbd85c3f20>

ax = plot_energy_balance(n, config["plotting"], start_date=str(summer_week[0]), end_date=str(summer_week[-1]))
ax.grid(axis='y')
ax.set_title("Electricity Balance - worst week summer")
ax2 = ax.twinx()
prov_max_price_series.loc[summer_week].plot(ax=ax2, color="red", lw=2, ls = "--", label= "Most exp Prov")
weighed_prices.loc[summer_week].plot(ax=ax2, color = "blue", lw=3, ls = ":",label = "National weighted price")
fig = ax.get_figure()
ax2.set_ylabel("PriceEur/MWh", color="red")
ax2.tick_params(axis='y', labelcolor='red')
# Move the existing legend to the right
handles, labels = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
ax.legend(handles + handles2, labels + labels2, loc='center left', bbox_to_anchor=(1.1, 0.5))

/home/ivanra/.conda/envs/pypsa-china/lib/python3.12/site-packages/pandas/plotting/_matplotlib/core.py:1798: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.
  ax.set_ylim(0, None)

<matplotlib.legend.Legend at 0x7fbbd864f260>

if snakemake.config["heat_coupling"]:
    ax = plot_energy_balance(n, config["plotting"], start_date=f"{PLANNING_YEAR}-03-31 21:00", end_date=f"{PLANNING_YEAR}-09-06 12:00:00", bus_carrier="heat")
    ax.set_title("Heat balance")
    ax2 = ax.twinx()
    prov_max_price_series.loc[f"{PLANNING_YEAR}-03-31 21:00":f"{PLANNING_YEAR}-09-06 12:00:00"].plot(ax=ax2, color="red", lw=2, ls = "--", label= "Most exp Prov")
    weighed_prices.loc[f"{PLANNING_YEAR}-03-31 21:00":f"{PLANNING_YEAR}-09-06 12:00:00"].plot(ax=ax2, color = "blue", lw=3, ls = ":",label = "National weighted price")
    prov_min_price_series.loc[f"{PLANNING_YEAR}-03-31 21:00":f"{PLANNING_YEAR}-09-06 12:00:00"].plot(ax=ax2, color="green", lw=2, ls = "--", label= "Least exp Prov")

    fig = ax.get_figure()
    ax2.set_ylabel("PriceEur/MWh", color="red")
    ax2.tick_params(axis='y', labelcolor='red')
    ax2.set_ylim([0, prov_max_price_series.loc[f"{PLANNING_YEAR}-03-31 21:00":f"{PLANNING_YEAR}-09-06 12:00:00"].max()*3])
    # Move the existing legend to the right
    handles, labels = ax.get_legend_handles_labels()
    handles2, labels2 = ax2.get_legend_handles_labels()
    ax.legend(handles + handles2, labels + labels2, loc='center left', bbox_to_anchor=(1.1, 0.5))

if snakemake.config["heat_coupling"]:
    ax = plot_energy_balance(n, config["plotting"], start_date=f"{PLANNING_YEAR}-01-01 00:00", end_date=f"{PLANNING_YEAR}-12-31 23:00:00", bus_carrier="heat")
    ax.set_title("Heat balance")

LOAD DURATION CURVES

from plot_time_series import plot_residual_load_duration_curve, plot_load_duration_curve

fig, ax = plt.subplots(1, 2, figsize=(10, 6), sharey=True)
plot_load_duration_curve(n, carrier="AC", ax = ax[0])
plot_residual_load_duration_curve(n, ax = ax[1])
fig.tight_layout()

from plot_time_series import plot_regional_load_durations

plot_regional_load_durations(n, carrier="AC", cmap = "viridis")

<Axes: xlabel='Hours', ylabel='Load [GW]'>

fix, ax = plt.subplots()
ds_AC = n.statistics.withdrawal(bus_carrier="AC", aggregate_time=False).loc[("Load", "-")]/1e3
ds_AC.plot(ax=ax, label="Electricity",  c="orange")
if snakemake.config["heat_coupling"]:
    ds_heat = n.statistics.withdrawal(bus_carrier="heat", aggregate_time=False).loc[("Load", "-")]/1e3
    ds_heat.plot(ax=ax, label="Heat", c = "blue")  
ax.legend()
ax.set_ylabel("EnergyDemand / GW")

Text(0, 0.5, 'EnergyDemand / GW')

Power flows map

from _pypsa_helpers import get_location_and_carrier
transmission = n.statistics.transmission(
        bus_carrier="AC",
        groupby=get_location_and_carrier,
        aggregate_time=False,
    )
transm_tot = transmission.T.sum().reset_index()
transm_tot = transm_tot.set_index("location").rename(columns={0:"HV transmission"}).sort_values(by="HV transmission")

ax = (
    (transmission/1e6)
    .groupby("location")
    .sum().loc[transm_tot.index[::-1]]
    .T.loc[f"{PLANNING_YEAR}-07-20":f"{PLANNING_YEAR}-07-27"]
    .plot.area(stacked=True, cmap="magma_r")
)
ax.legend(loc="center left", bbox_to_anchor=(1, 0.5), title="Prov", ncols=2)
ax.set_ylabel("Transmission [TWh]")

Text(0, 0.5, 'Transmission [TWh]')

ax = transm_tot.plot.bar(y="HV transmission")
ax.set_ylabel("HV Transmission / MWHr")

Text(0, 0.5, 'HV Transmission / MWHr')

Plot cap factors time series

capacity_factors = n.statistics.capacity_factor(aggregate_time=False).loc[["Generator"]].droplevel(0).T

colors_lowered = {k.lower().rstrip():v for k,v in config["plotting"]["tech_colors"].items()}
cap_colors = {k: colors_lowered.get(k.lower().rstrip(), "lightgrey") for k in [c for c in capacity_factors.columns]}
axes = capacity_factors.dropna(axis=1, how="all").fillna(0).plot(subplots=True, figsize=(10,12), color= cap_colors)
for ax in axes:
    ax.set_ylim([0,1.1])
    ax.set_yticks([0,0.3, 0.6,1])

PLANNING_YEAR

2035

weighting = n.snapshot_weightings.iloc[0,0]

region_rev = n.statistics.revenue(groupby = ["location", "carrier"], bus_carrier="AC", aggregate_time=False).fillna(0)
rev_regional = region_rev[(region_rev<0).all(axis=1)].groupby("location").sum().T
prod_reg = n.statistics.energy_balance(groupby = ["location", "carrier"], bus_carrier="AC", aggregate_time=False)*weighting
consum_regional = prod_reg.query("component=='Load'").groupby("location").sum().T
balance = prod_reg.groupby("location").sum().T
supp_regional = prod_reg.query("component!='Load'").groupby("location").sum().T
ax = (rev_regional/consum_regional).plot(cmap="viridis")
ax.set_ylabel("Expenditure/Withdrawal [€/MWh]")
ax.set_xlabel("Time")
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncols=2)

<matplotlib.legend.Legend at 0x7fbbd81ba510>

import numpy as np

import matplotlib.pyplot as plt

data = pd.concat([supp_regional.sum(), -1*consum_regional.sum(), balance.sum()], axis=1)
data.columns = ["Supply", "Consum", "Balance"]
data = data.query("index != ''") # no location assigned
fig, ax = plt.subplots(figsize=(10, 6))
bar_width = 0.25
x = np.arange(len(data.index))
data = data/1e6
ax.bar(x - bar_width, data["Supply"], width=bar_width, label="Supply")
ax.bar(x, data["Consum"], width=bar_width, label="Consum")
ax.bar(x + bar_width, data["Balance"], width=bar_width, label="Balance")

ax.set_xticks(x)
ax.set_xticklabels(data.index, rotation =70, ha ='right', fontsize=11)
ax.legend()
ax.set_ylabel("Energy [TWh]", fontsize=12)
fig.tight_layout()
plt.show()

max_val = max(supp_regional.max().max(), -1*consum_regional.max().max())

fig, axes = plt.subplots(6,6, sharex=True, sharey=True, figsize=(20,20))
norm_price =  n.buses_t["marginal_price"][PROV_NAMES].loc[f"{PLANNING_YEAR}-07-22":f"{PLANNING_YEAR}-07-28"]
norm_price/= norm_price.max()
norm_price*=(consum_regional*-1e-6).loc[f"{PLANNING_YEAR}-07-22":f"{PLANNING_YEAR}-07-28"].max().max()*0.8
consum_regional = consum_regional[PROV_NAMES]
for i,c in enumerate(consum_regional.columns):
    k, l = (i+4)//6, (i+4)%6
    (consum_regional[c]*-1e-3).loc[f"{PLANNING_YEAR}-07-22":f"{PLANNING_YEAR}-07-28"].plot(ax=axes[k,l])
    (supp_regional[c]*1e-3).loc[f"{PLANNING_YEAR}-07-22":f"{PLANNING_YEAR}-07-28"].plot(ax=axes[k,l], color = "orange", alpha = 0.5)
    axes[k,l].set_title(c)
    axes[k,l].set_ylim(0, max_val*1e-3)

    if l ==0:
        axes[k,l].set_ylabel("Withdrawal or supply [GWh]")
    if k==0:
        axes[k,l].set_xlabel("Time")
axes[0, 4].legend(["Withdrawal (blue)", "Supply (orange)"])
fig.tight_layout()
fig.subplots_adjust(hspace=0.15, wspace=0.1)

exports_regional =balance
max_val = balance.max().max()
fig, axes = plt.subplots(6,6, sharex=True, sharey=True, figsize=(20,20))
norm_price =  n.buses_t["marginal_price"][PROV_NAMES].loc[f"{PLANNING_YEAR}-12-22":f"{PLANNING_YEAR}-12-28"]
norm_price/= norm_price.max()
norm_price*= max_val*0.3e-3
for i,c in enumerate(consum_regional.columns):
    k, l = (i+4)//6, (i+4)%6
    exports = (exports_regional[c]*1e-3).loc[f"{PLANNING_YEAR}-12-22":f"{PLANNING_YEAR}-12-28"]
    exports.plot(ax=axes[k,l], color = "orange", alpha = 1)
    norm_price[c].plot(ax=axes[k,l], color = "green", alpha = 0.5, legend = False, ls ="--")

    axes[k,l].set_title(c)
    # axes[k,l].set_ylim( exports.min(), exports.max())
    axes[k,l].axhline(0, color="black", lw=1)
    if l ==0:
        axes[k,l].set_ylabel("Balance [GWh] \n Norm Price (green)")
    if k==0:
        axes[k,l].set_xlabel("Time")
axes[0, 4].legend(["Exports"])
fig.tight_layout()
fig.subplots_adjust(hspace=0.2, wspace=0.1)

from constants import PROV_NAMES 
ax = n.buses_t["marginal_price"][PROV_NAMES].loc[f"{PLANNING_YEAR}-07-22":f"{PLANNING_YEAR}-07-28"].plot(cmap="viridis",alpha =0.7, logy=True)
ax.set_ylabel("Marginal Price [€/MWh]")
ax.set_xlabel("Time")
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), title="Province", ncol=2)

<matplotlib.legend.Legend at 0x7fbbd3b989e0>

rev_and_exp = n.statistics.revenue(bus_carrier="AC", aggregate_time=False).fillna(0).groupby(level=1).sum().T
prod = n.statistics.energy_balance(bus_carrier="AC", aggregate_time=False).fillna(0)
prod = prod[~(prod<=0).all(axis=1)]
prod=prod.clip(lower=1e-5).groupby(level=1).sum().T
hourly_price = rev_and_exp.clip(lower=0)/prod
hourly_price = hourly_price.loc[:,(hourly_price>0).all(axis=0)]
ax = hourly_price.plot(logy=False, cmap = "viridis")
ax.set_ylim([1, hourly_price.max().max()*1.2])
ax.set_ylabel("Revenue/Supply [€/MWh]")
ax.set_xlabel("Time")
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))

ax2 = (prod/1e6).plot(cmap="viridis", logy=False)
ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5), title="Carrier", ncol=2)
ax2.set_ylabel("Supply [TWh]")
ax2.set_xlabel("Time")
ax2.legend(loc='center left', bbox_to_anchor=(1, 0.5))

<matplotlib.legend.Legend at 0x7fbbd253bf20>

colors_lowered = {k.lower().rstrip():v for k,v in config["plotting"]["tech_colors"].items()}
cap_colors = {k: colors_lowered.get(k.lower().rstrip(), "lightgrey") for k in [c for c in capacity_factors.columns]}

# Apply rolling moving average filter
capacity_factors_smoothed = capacity_factors.rolling(window=24, min_periods=1).mean()

axes = capacity_factors_smoothed.dropna(axis=1, how="all").fillna(0).plot(subplots=True, figsize=(10,12), color= cap_colors)
for ax in axes:
    ax.set_ylim([0,1.1])
    ax.set_yticks([0,0.3, 0.6,1])

COSTS & prices

Price duration curve

n.buses.carrier[n.buses.carrier=="H2"].index.values

array(['Anhui H2', 'Beijing H2', 'Chongqing H2', 'Fujian H2', 'Gansu H2',
       'Guangdong H2', 'Guangxi H2', 'Guizhou H2', 'Hainan H2',
       'Hebei H2', 'Heilongjiang H2', 'Henan H2', 'Hubei H2', 'Hunan H2',
       'InnerMongolia H2', 'Jiangsu H2', 'Jiangxi H2', 'Jilin H2',
       'Liaoning H2', 'Ningxia H2', 'Qinghai H2', 'Shaanxi H2',
       'Shandong H2', 'Shanghai H2', 'Shanxi H2', 'Sichuan H2',
       'Tianjin H2', 'Tibet H2', 'Xinjiang H2', 'Yunnan H2',
       'Zhejiang H2'], dtype=object)

ntwk_el_price = -1*n.statistics.revenue(bus_carrier="AC", aggregate_time=False, comps="Load")/n.statistics.withdrawal(bus_carrier="AC", aggregate_time=False, comps="Load")
ntwk_el_price.T.Load.sort_values(ascending=False).reset_index(drop=True).plot()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/p/tmp/ivanra/anaconda/ipykernel_2927777/276366657.py in ?()
      1 ntwk_el_price = -1*n.statistics.revenue(bus_carrier="AC", aggregate_time=False, comps="Load")/n.statistics.withdrawal(bus_carrier="AC", aggregate_time=False, comps="Load")
----> 2 ntwk_el_price.T.Load.sort_values(ascending=False).reset_index(drop=True).plot()

~/.conda/envs/pypsa-china/lib/python3.12/site-packages/pandas/core/generic.py in ?(self, name)
   6295             and name not in self._accessors
   6296             and self._info_axis._can_hold_identifiers_and_holds_name(name)
   6297         ):
   6298             return self[name]
-> 6299         return object.__getattribute__(self, name)

AttributeError: 'DataFrame' object has no attribute 'Load'

carrier = "AC"
carrier_buses = n.buses.carrier[n.buses.carrier == carrier].index.values
nodal_prices = n.buses_t.marginal_price[carrier_buses]

import seaborn as sns

def plot_price_map(network: pypsa.Network, carrier = "AC", ax: object = None):

    if not ax:
        fig, ax = plt.subplots(figsize=(20, 8))
    else: 
        fig = ax.get_figure()

    carrier_buses = network.buses.carrier[network.buses.carrier == carrier].index.values
    nodal_prices = network.buses_t.marginal_price[carrier_buses]
    # Normalize nodal_prices with log transformation
    normalized_prices = np.log10(nodal_prices.clip(lower=0.1))

    # Create a heatmap of normalized nodal_prices
    sns.heatmap(normalized_prices.reset_index(drop=True).T, cmap="viridis", cbar_kws={'label': 'Log-Transformed Price [€/MWh]'}, ax=ax)

    # Customize the plot
    ax.set_title("Heatmap of Log-Transformed Nodal Prices")
    ax.set_xlabel("Time")
    ax.set_ylabel("Nodes")
    fig.tight_layout()

    return

plot_price_map(n)

from plot_time_series import plot_price_duration_curve, plot_price_duration_by_node, plot_price_duration_by_carrier

plot_price_duration_by_node(n, carrier="AC")

<Axes: >

def plot_load_duration_by_node(network: pypsa.Network, carrier: str = "AC", logy= True, y_lower = 1e-3, fig_shape=(8, 4)):
  
    if carrier == "AC":
        suffix = ""
    else:
        suffix = f" {carrier}"
    
    carrier_buses = n.buses.carrier[n.buses.carrier == carrier].index.values
    nodal_prices = n.buses_t.marginal_price[carrier_buses]

    if fig_shape[0] * fig_shape[1] < len(nodal_prices.columns):
        raise ValueError(
            f"Figure shape {fig_shape} is too small for {len(nodal_prices.columns)} regions. "
            + "Please increase the number of subplots."
        )
    fig, axes = plt.subplots(fig_shape[0], fig_shape[1], sharex=True, sharey=True, figsize=(12,12))

    # region by region sorting of prices 
    for i, region in enumerate(nodal_prices.columns):
        reg_pr = nodal_prices[region]
        reg_pr.sort_values(ascending=False).reset_index(drop=True).plot(ax=axes[i//4, i%fig_shape[1]], label=region)
        axes[i//4, i%fig_shape[1]].set_title(region, fontsize=10)
        if logy:
            axes[i//4, i%fig_shape[1]].semilogy()
        if y_lower:
            axes[i//4, i%fig_shape[1]].set_ylim(y_lower, reg_pr.max()*1.2)
        elif reg_pr.min() >1e-5 and not logy: 
            axes[i//4, i%fig_shape[1]].set_ylim(0, reg_pr.max()*1.2)
    fig.tight_layout(h_pad=0.2, w_pad=0.2)
    for ax in axes.flat:
        # Remove all x-tick labels except the largest value
        xticks = ax.get_xticks()
        if len(xticks) > 0:
            ax.set_xticks([xticks[0], xticks[-1]])
            ax.set_xticklabels([f"{xticks[0]:.0f}", f"{xticks[-1]:.0f}"])

    return ax

plot_load_duration_by_node(n, carrier="AC", logy=True, fig_shape=(8, 4))

<Axes: >

plot_load_duration_by_node(n, carrier="H2", logy=False, fig_shape=(8, 4), y_lower=0)

<Axes: >

from plot_summary_all import plot_prices
paths = [os.path.join(results_dir, "summary", f"ntwk_{yr}") for yr in snakemake.config["scenario"]["planning_horizons"]]
data_paths = {
        "energy": [os.path.join(p, "energy.csv") for p in paths],
        "costs": [os.path.join(p, "costs.csv") for p in paths],
        "co2_price": [os.path.join(p, "metrics.csv") for p in paths],
        "time_averaged_prices": [os.path.join(p, "time_averaged_prices.csv") for p in paths],
        "weighted_prices": [os.path.join(p, "weighted_prices.csv") for p in paths],
        "co2_balance": [os.path.join(p, "co2_balance.csv") for p in paths],
        "energy_supply": [os.path.join(p, "supply_energy.csv") for p in paths],
        "capacity": [os.path.join(p, "capacities.csv") for p in paths],
    }

fig, ax = plt.subplots()
plot_prices(
    data_paths["time_averaged_prices"],
    config["plotting"],
    fig_name=None,
    ax=ax
)

fig, ax = plt.subplots()
plot_prices(
    data_paths["weighted_prices"],
    config["plotting"],
    fig_name=None,
    ax=ax,
)
# ax.semilogy()

stores_inflow = n.stores_t.p.sum()
h2_stores= stores_inflow.loc[[c for c in stores_inflow.index if c.find("H2 Store")!=-1]]
h2_stores_inflow = h2_stores.where(h2_stores>0,0).sum()
h2_stores_outflow = h2_stores.where(h2_stores<0,0).sum()

STATS

n.statistics.optimal_capacity( groupby = pypsa.statistics.get_carrier_and_bus_carrier, nice_names=True)

component    carrier               bus_carrier    
Store        Battery Storage       Battery Storage    1.110708e+07
             H2                    H2                 3.332356e+02
             gas                   gas                3.667870e+08
             stations              stations           4.535627e+07
Link         AC                    AC                 1.731581e+06
             Battery Storage       AC                 1.989285e+06
             H2 Electrolysis       AC                 1.664580e+00
             H2 Fuel Cell          H2                 6.577840e+00
             H2 pipeline           H2                 5.202289e+01
             Hydroelectricity      stations           1.403220e+05
             Open-Cycle Gas        gas                1.210529e+05
             battery discharger    Battery Storage    2.030305e+06
             stations              stations           2.483820e+08
StorageUnit  Pumped Hydro Storage  AC                 3.639000e+04
Generator    Coal Power Plant      AC                 7.889498e+05
             Hydroelectricity      AC                 2.253780e+05
             Offshore Wind         AC                 7.750480e+03
             Onshore Wind          AC                 8.109340e+04
             Solar                 AC                 1.099864e+07
             gas                   gas                1.222053e+08
             hydro_inflow          stations           5.408039e+06
             nuclear               AC                 5.464707e+04
dtype: float64

def get_solver_tolerance(config: dict, tol_name = "BarConvTol")->float:
    """get the solver tolerance from the config 

    Args:
        config (dict): the config
        tol_name (str): the name of the tolerance option. Defaults to "BarConvTol"

    Returns:
        float: the value
    """    
    solver_opts = config["solving"]["solver"]["options"]
    return config["solving"]["solver_options"][solver_opts][tol_name]

def find_numerical_zeros(n, config, tolerance_name = "BarConvTol")->list:
    """
    Identify numerical zeros in the network's optimization results.

    This function checks for numerical zeros in the network's optimization results, 
    such as link capacities or weighted prices, based on a specified solver tolerance.

    Args:
        n (pypsa.Network): The PyPSA network object containing optimization results.
        config (dict): Configuration dictionary containing solver options.
        tolerance_name (str): The name of the solver tolerance option to use. Defaults to "BarConvTol".

    Returns:
        list: A list of items (e.g., links or buses) where numerical zeros are detected.
    """
 
    tol = get_solver_tolerance(config, tolerance_name)
    threshold = n.objective*float(tol)
    
    costs = pd.concat([n.statistics.expanded_capex(), n.statistics.opex()],axis=1)
    return costs.fillna(0).sum(axis=1).loc[costs.sum(axis=1) < threshold].index
    
num_zeros = find_numerical_zeros(n, config, tolerance_name = "BarConvTol")

bus_carrier = "AC"
n.loads.carrier = "load"
n.carriers.loc["load", ["nice_name", "color"]] = "Load", "#110d63"
colors = n.carriers.set_index("nice_name").color.where(
    lambda s: s != "", "lightgrey"
)

def rename_index(ds):
    specific = ds.index.map(lambda x: f"{x[1]}\n({x[0]})")
    generic = ds.index.get_level_values("carrier")
    duplicated = generic.duplicated(keep=False)
    index = specific.where(duplicated, generic)
    return ds.set_axis(index)

def plot_static_per_carrier(ds, ax, drop_zero=True):
    if drop_zero:
        ds = ds[ds != 0]
    ds = ds.dropna()
    c = colors[ds.index.get_level_values("carrier")]
    ds = ds.pipe(rename_index)
    label = f"{ds.attrs['name']} [{ds.attrs['unit']}]"
    ds.plot.barh(color=c.values, xlabel=label, ax=ax)
    ax.grid(axis="y")

fig, ax = plt.subplots()
ds = n.statistics.capacity_factor(bus_carrier=bus_carrier).dropna()
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.installed_capacity(bus_carrier=bus_carrier).dropna()
if "Line" in ds.index:
    ds = ds.drop("Line")
ds = ds.drop(("Generator", "Load"), errors="ignore")
ds = ds / 1e3
ds.attrs["unit"] = "GW"
plot_static_per_carrier(ds.abs(), ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.optimal_capacity(bus_carrier=bus_carrier)
if "Line" in ds.index:
    ds = ds.drop("Line")
ds = ds.drop(("Generator", "Load"), errors="ignore")
ds = ds.abs() / 1e3
ds.attrs["unit"] = "GW"
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.capex(bus_carrier=bus_carrier)
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.opex(bus_carrier=bus_carrier)
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.curtailment(bus_carrier=bus_carrier)
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.supply(bus_carrier=bus_carrier)
if "Line" in ds.index:
    ds = ds.drop("Line")
ds = ds / 1e6
ds.attrs["unit"] = "TWh"
plot_static_per_carrier(ds, ax)
plt.show()

if snakemake.config["heat_coupling"]:
    fig, ax = plt.subplots()
    ds = n.statistics.supply(bus_carrier="heat")
    if "Line" in ds.index:
        ds = ds.drop("Line")
    ds = ds / 1e6
    ds.attrs["unit"] = "TWh"
    plot_static_per_carrier(ds, ax)
    plt.show()

fig, ax = plt.subplots()
ds = n.statistics.withdrawal(bus_carrier=bus_carrier)
if "Line" in ds.index:
    ds = ds.drop("Line")
ds = ds / -1e6
ds.attrs["unit"] = "TWh"
plot_static_per_carrier(ds, ax)
plt.show()

fig, ax = plt.subplots()
ds = n.statistics.market_value(bus_carrier=bus_carrier)
plot_static_per_carrier(ds, ax)
plt.show()

def calc_lcoe(n: pypsa.Network, grouper = pypsa.statistics.get_carrier_and_bus_carrier, **kwargs)->pd.DataFrame:
    """calculate the LCOE for the network: (capex+opex)/supply.

    Args:
        n (pypsa.Network): the network for which LCOE is to be calaculated
        grouper (function | list, optional): function to group the data in network.statistics.
                Overwritten if groupby is passed in kwargs.
                Defaults to pypsa.statistics.get_carrier_and_bus_carrier. 
        **kwargs: other arguments to be passed to network.statistics
    Returns:
        pd.DataFrame: The LCOE for the network  with or without brownfield CAPEX. MV and delta

    """    
    if "groupby" in kwargs:
        grouper = kwargs.pop("groupby")

    rev= n.statistics.revenue(groupby=grouper,**kwargs)
    opex = n.statistics.opex(groupby=grouper,**kwargs)
    capex = n.statistics.expanded_capex(groupby=grouper,**kwargs)
    tot_capex = n.statistics.capex(groupby=grouper,**kwargs)
    supply = n.statistics.supply(groupby=grouper,**kwargs)

    profits = pd.concat([opex, capex, tot_capex, rev, supply], axis=1,  keys = ["OPEX", "CAPEX", "CAPEX_wBROWN", "Revenue", "supply"]).fillna(0)
    profits["rev-costs"]=profits.apply(lambda row: row.Revenue-row.CAPEX-row.OPEX, axis=1)
    profits["LCOE"] = profits.apply(lambda row: (row.CAPEX+row.OPEX)/row.supply, axis=1)
    profits["LCOE_wbrownfield"] = profits.apply(lambda row: (row.CAPEX_wBROWN+row.OPEX)/row.supply, axis=1)
    profits["MV"] = profits.apply(lambda row: row.Revenue/row.supply, axis=1)
    profits["profit_pu"]=profits["rev-costs"]/profits.supply
    profits.sort_values("profit_pu", ascending=False, inplace=True)
    
    return profits[profits.supply>0]

rev_costs = calc_lcoe(n, groupby=None)
rev_costs = calc_lcoe(n, groupby=None)
ds = rev_costs["LCOE"]
ds.attrs={"name":"LCOE", "unit":"€/MWh"}
fig, ax = plt.subplots()
plot_static_per_carrier(ds[~ds.index.isin(num_zeros)], ax)
plt.show()

rev_costs = calc_lcoe(n, groupby=None)
ds = rev_costs["profit_pu"]
ds.attrs={"name":"MV - LCOE", "unit":"€/MWh"}
fig, ax = plt.subplots()
plot_static_per_carrier(ds[~ds.index.isin(num_zeros)], ax)
plt.show()

WARNING:pypsa.statistics:Network has multiple bus carriers which are aggregated together. To separate bus carriers set `bus_carrier` or use groupers like `get_carrier_and_bus_carrier`.
/tmp/ipykernel_1698823/2397917093.py:25: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:26: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:27: RuntimeWarning:

divide by zero encountered in scalar divide

WARNING:pypsa.statistics:Network has multiple bus carriers which are aggregated together. To separate bus carriers set `bus_carrier` or use groupers like `get_carrier_and_bus_carrier`.
/tmp/ipykernel_1698823/2397917093.py:25: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:26: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:27: RuntimeWarning:

divide by zero encountered in scalar divide

WARNING:pypsa.statistics:Network has multiple bus carriers which are aggregated together. To separate bus carriers set `bus_carrier` or use groupers like `get_carrier_and_bus_carrier`.
/tmp/ipykernel_1698823/2397917093.py:25: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:26: RuntimeWarning:

invalid value encountered in scalar divide

/tmp/ipykernel_1698823/2397917093.py:27: RuntimeWarning:

divide by zero encountered in scalar divide

Network topology

irrelevant as don't have lines

n.determine_network_topology()
n.sub_networks["n_branches"] = [
    len(sn.branches()) for sn in n.sub_networks.obj
]
n.sub_networks["n_buses"] = [len(sn.buses()) for sn in n.sub_networks.obj]

CO2 costs

if "emission_prices" in n.meta:
    em_price_gas = float(n.statistics.supply(comps="Generator")["gas"]*n.meta["emission_prices"]["co2"]*0.2/0.43)
    print(f"{em_price_gas:.2e}")
n.global_constraints

WARNING:pypsa.statistics:Network has multiple bus carriers which are aggregated together. To separate bus carriers set `bus_carrier` or use groupers like `get_carrier_and_bus_carrier`.

2.61e+08

attribute	type	investment_period	carrier_attribute	sense	constant	mu
GlobalConstraint

costs.rename(columns={"Unnamed: 0":"expend", "Unnamed: 1": "carrier"}, inplace=True)
costs_ = costs.set_index(["expend", "carrier"]).T
costs_.T.groupby("expend").sum().T.sort_index().plot(kind="bar", stacked=True)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[59], line 1
----> 1 costs.rename(columns={"Unnamed: 0":"expend", "Unnamed: 1": "carrier"}, inplace=True)
      2 costs_ = costs.set_index(["expend", "carrier"]).T
      3 costs_.T.groupby("expend").sum().T.sort_index().plot(kind="bar", stacked=True)

NameError: name 'costs' is not defined

costs_s = costs_.T.sum()
dt = 5
costs_s.index = costs_s.index.astype(int)
co2_costs = co2.set_index("Year")["costs bnE"]
costs_df = pd.DataFrame({'Costs pw bn eur': costs_s, 'CO2 Costs bn eur': co2_costs})
costs_df["Costs no co2 bn eur"] = costs_df["Costs pw bn eur"] - costs_df["CO2 Costs bn eur"]
costs_df["discount"]=costs_df.apply(lambda x: 1.02**(x.name-min(costs_df.index)), axis =1)
discounted_costs = costs_df.apply(lambda x: x/x.discount, axis=1)
discounted_costs, costs_df.apply(lambda x: x.sum()*5 ), discounted_costs.apply(lambda x: x.sum()*5 )

(      Costs pw bn eur  CO2 Costs bn eur  Costs no co2 bn eur  discount
 2020       426.415207         54.897743           371.517464       1.0
 2025       459.913593         72.058113           387.855480       1.0
 2030       450.013123         55.538548           394.474575       1.0
 2035       428.357785         35.076815           393.280970       1.0
 2040       397.500409         19.424611           378.075799       1.0
 2045       375.332390         13.015016           362.317373       1.0
 2050       353.459437          8.496730           344.962707       1.0
 2055       368.593595         18.899338           349.694256       1.0
 2060       369.697213          0.000000           369.697213       1.0,
 Costs pw bn eur        27297.031714
 CO2 Costs bn eur        1763.846907
 Costs no co2 bn eur    25533.184807
 discount                  69.073939
 dtype: float64,
 Costs pw bn eur        18146.413756
 CO2 Costs bn eur        1387.034575
 Costs no co2 bn eur    16759.379181
 discount                  45.000000
 dtype: float64)

discounted_costs["Costs pw bn eur"].plot(kind="bar", stacked=True, color="blue", label="Costs pw bn eur")

<Axes: >

ax = costs_.T.unstack(level=0).T.sort_index().plot(
    kind='bar', 
    stacked=True, 
    figsize=(12, 6), 
    width=0.8, 
    legend=True)


# Customize the legend with a dictionary, including bbox_to_anchor
legend_kw = {
    'title': 'Carrier',          # Title of the legend
    'loc': 'upper left',              # Legend location (usually 'upper left' or 'best')
    'fontsize': 'small',              # Font size for the legend
    'frameon': False,                 # Remove the legend's frame
    'ncol': 2,                        # Number of columns in the legend
    'bbox_to_anchor': (1.05, 1),      # Adjust the legend's position (x, y)
    'borderaxespad': 0.5              # Padding between the legend and plot
}

# Apply the legend customization
ax.legend(**legend_kw)

<matplotlib.legend.Legend at 0x7f0e63723c20>

Validation

import seaborn as sns

capex_ = n.statistics.expanded_capex().sum()
installed_capex_ = n.statistics.installed_capex().sum()
opex_ = n.statistics.opex().sum()
costs_ = pd.DataFrame(
    {
        "OPEX": opex_,
        "Installed CAPEX": installed_capex_,
        "CAPEX": capex_,
        "total": capex_ + installed_capex_ + opex_,
    }, index = ["Costs"]
).stack()
revenue_ = n.statistics.revenue(comps="Load", bus_carrier="AC").sum()
costs_.loc[("Revenue", "AC")]=revenue_*-1
all_ = pd.concat({f"{PLANNING_YEAR} budget":costs_}, names=["CO2 control"])
all_.index.names = ["CO2 control", "Cat", "Type"]
all_ = pd.DataFrame(all_, columns = ["Value"])


sns.catplot(all_,x="Cat", y="Value",  hue="Type", col = "CO2 control", dodge=True, kind="bar", alpha =0.8)

<seaborn.axisgrid.FacetGrid at 0x7f0e588c20c0>

rev= n.statistics.revenue(groupby=pypsa.statistics.get_carrier_and_bus_carrier)
opex = n.statistics.opex(groupby=pypsa.statistics.get_carrier_and_bus_carrier)
capex = n.statistics.expanded_capex(groupby=pypsa.statistics.get_carrier_and_bus_carrier)
supply = n.statistics.supply(groupby=pypsa.statistics.get_carrier_and_bus_carrier)
index = rev.index.union(opex.index).union(capex.index)
rents = pd.concat([opex, capex, rev, supply], axis=1,  keys = ["OPEX", "CAPEX", "Revenue", "supply"]).fillna(0)
rents["delta"]=rents.apply(lambda row: row.Revenue-row.CAPEX-row.OPEX, axis=1)
rents["delta_pu"]=rents.delta/rents.supply
rents.sort_values("delta_pu", ascending=False, inplace=True)

supply

component    carrier               bus_carrier    
Generator    Coal Power Plant      AC                 1.852240e+04
             Offshore Wind         AC                 1.129338e+09
             Onshore Wind          AC                 4.365634e+08
             Solar                 AC                 1.216664e+10
             gas                   gas                1.491579e+09
             hydro_inflow          stations           3.517783e+08
             nuclear               AC                 1.059834e+09
Link         AC                    AC                 6.748333e+09
             Battery Storage       Battery Storage    6.497103e+09
             H2 Electrolysis       H2                 6.189096e+07
             H2 Fuel Cell          AC                 2.978052e+07
             H2 pipeline           H2                 1.746955e+08
             Hydroelectricity      AC                 4.797237e+08
                                   stations           2.684737e+08
             Open-Cycle Gas        AC                 6.413986e+08
             Sabatier              gas                4.756363e+04
             battery discharger    AC                 6.365834e+09
             stations              stations           3.586407e+07
StorageUnit  Pumped Hydro Storage  AC                 4.595525e+07
Store        Battery Storage       Battery Storage    6.138904e+09
             H2                    H2                 5.333911e+07
             gas                   gas                1.075529e+09
             stations              stations           3.520498e+08
dtype: float64

rev

component    carrier               bus_carrier    
Generator    Coal Power Plant      AC                 1.937514e+06
             Offshore Wind         AC                 5.746152e+10
             Onshore Wind          AC                 2.927233e+10
             Solar                 AC                 3.365797e+11
             gas                   gas                6.775035e+10
             hydro_inflow          stations           9.585767e+09
             nuclear               AC                 6.935263e+10
Link         AC                    AC                 6.998466e+10
             Battery Storage       AC                -1.565939e+11
                                   Battery Storage    1.874158e+11
             H2 Electrolysis       AC                -1.388081e+08
                                   H2                 6.231078e+08
             H2 Fuel Cell          AC                 2.918551e+09
                                   H2                -2.052169e+09
             H2 pipeline           AC                -3.677823e+07
                                   H2                 3.823994e+08
             Hydroelectricity      AC                 3.831391e+10
                                   stations          -1.515727e+10
             Open-Cycle Gas        AC                 8.551149e+10
                                   gas               -6.775238e+10
             Sabatier              H2                -8.002653e+05
                                   gas                2.111894e+06
             battery discharger    AC                 2.940412e+11
                                   Battery Storage   -2.940339e+11
             stations              stations          -2.021822e+06
StorageUnit  Pumped Hydro Storage  AC                 2.596869e+09
Store        Battery Storage       Battery Storage    1.066181e+11
             H2                    H2                 1.047455e+09
             gas                   gas                8.199906e+03
             stations              stations           5.573505e+09
Load         Load                  AC                -8.292652e+11
dtype: float64

rents[rents.supply>1e5].droplevel(0).sort_values("delta_pu", ascending=False).plot.bar( y="delta_pu", stacked=True, figsize=(10, 6), ylabel="Rev-Costs Eur/MWh")

<Axes: xlabel='carrier,bus_carrier', ylabel='Rev-Costs Eur/MWh'>

n.links[n.links.carrier=="gas OCGT"]

	bus0	bus1	type	carrier	efficiency	active	build_year	lifetime	p_nom	p_nom_mod	...	down_time_before	ramp_limit_up	ramp_limit_down	ramp_limit_start_up	ramp_limit_shut_down	p_nom_opt	bus2	efficiency2	location	plottable
Link
Anhui OCGT	Anhui gas	Anhui		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.210749e-08		1.0	Anhui	True
Beijing OCGT	Beijing gas	Beijing		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.219836e-08		1.0	Beijing	True
Chongqing OCGT	Chongqing gas	Chongqing		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.180835e-08		1.0	Chongqing	True
Fujian OCGT	Fujian gas	Fujian		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.135034e-08		1.0	Fujian	True
Gansu OCGT	Gansu gas	Gansu		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.268867e-08		1.0	Gansu	True
Guangdong OCGT	Guangdong gas	Guangdong		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.160233e-08		1.0	Guangdong	True
Guangxi OCGT	Guangxi gas	Guangxi		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	9.593560e-09		1.0	Guangxi	True
Guizhou OCGT	Guizhou gas	Guizhou		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.133800e-08		1.0	Guizhou	True
Hainan OCGT	Hainan gas	Hainan		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.128516e-08		1.0	Hainan	True
Hebei OCGT	Hebei gas	Hebei		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.281898e-08		1.0	Hebei	True
Heilongjiang OCGT	Heilongjiang gas	Heilongjiang		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.200628e-08		1.0	Heilongjiang	True
Henan OCGT	Henan gas	Henan		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.225126e-08		1.0	Henan	True
Hubei OCGT	Hubei gas	Hubei		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.137650e-08		1.0	Hubei	True
Hunan OCGT	Hunan gas	Hunan		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.307639e-08		1.0	Hunan	True
InnerMongolia OCGT	InnerMongolia gas	InnerMongolia		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.214625e-08		1.0	InnerMongolia	True
Jiangsu OCGT	Jiangsu gas	Jiangsu		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.074345e-08		1.0	Jiangsu	True
Jiangxi OCGT	Jiangxi gas	Jiangxi		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.296391e-08		1.0	Jiangxi	True
Jilin OCGT	Jilin gas	Jilin		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.237052e-08		1.0	Jilin	True
Liaoning OCGT	Liaoning gas	Liaoning		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.239674e-08		1.0	Liaoning	True
Ningxia OCGT	Ningxia gas	Ningxia		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.192159e-08		1.0	Ningxia	True
Qinghai OCGT	Qinghai gas	Qinghai		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.238705e-08		1.0	Qinghai	True
Shaanxi OCGT	Shaanxi gas	Shaanxi		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.292193e-08		1.0	Shaanxi	True
Shandong OCGT	Shandong gas	Shandong		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.279889e-08		1.0	Shandong	True
Shanghai OCGT	Shanghai gas	Shanghai		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.178126e-08		1.0	Shanghai	True
Shanxi OCGT	Shanxi gas	Shanxi		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.178651e-08		1.0	Shanxi	True
Sichuan OCGT	Sichuan gas	Sichuan		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.208614e-08		1.0	Sichuan	True
Tianjin OCGT	Tianjin gas	Tianjin		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.196493e-08		1.0	Tianjin	True
Tibet OCGT	Tibet gas	Tibet		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	7.946448e+02		1.0	Tibet	True
Xinjiang OCGT	Xinjiang gas	Xinjiang		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.039274e-08		1.0	Xinjiang	True
Yunnan OCGT	Yunnan gas	Yunnan		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.160114e-08		1.0	Yunnan	True
Zhejiang OCGT	Zhejiang gas	Zhejiang		gas OCGT	0.4	True	0	25.0	0.0	0.0	...	0	NaN	NaN	1.0	1.0	1.102403e-08		1.0	Zhejiang	True
Jiangsu OCGT-2000.0	Jiangsu gas	Jiangsu OCGT		gas OCGT	0.4	True	2000	25.0	127.5	0.0	...	0	NaN	NaN	1.0	1.0	1.275000e+02		1.0	Jiangsu	False
Tianjin OCGT-2000.0	Tianjin gas	Tianjin OCGT		gas OCGT	0.4	True	2000	25.0	135.0	0.0	...	0	NaN	NaN	1.0	1.0	1.350000e+02		1.0	Tianjin	False
Zhejiang OCGT-2000.0	Zhejiang gas	Zhejiang OCGT		gas OCGT	0.4	True	2000	25.0	1715.0	0.0	...	0	NaN	NaN	1.0	1.0	1.715000e+03		1.0	Zhejiang	False
Guangdong OCGT-2005.0	Guangdong gas	Guangdong OCGT		gas OCGT	0.4	True	2005	25.0	4187.5	0.0	...	0	NaN	NaN	1.0	1.0	4.187500e+03		1.0	Guangdong	False
Hainan OCGT-2005.0	Hainan gas	Hainan OCGT		gas OCGT	0.4	True	2005	25.0	1475.0	0.0	...	0	NaN	NaN	1.0	1.0	1.475000e+03		1.0	Hainan	False
Jiangsu OCGT-2005.0	Jiangsu gas	Jiangsu OCGT		gas OCGT	0.4	True	2005	25.0	2925.0	0.0	...	0	NaN	NaN	1.0	1.0	2.925000e+03		1.0	Jiangsu	False
Shanghai OCGT-2005.0	Shanghai gas	Shanghai OCGT		gas OCGT	0.4	True	2005	25.0	1800.0	0.0	...	0	NaN	NaN	1.0	1.0	1.800000e+03		1.0	Shanghai	False
Zhejiang OCGT-2005.0	Zhejiang gas	Zhejiang OCGT		gas OCGT	0.4	True	2005	25.0	3670.0	0.0	...	0	NaN	NaN	1.0	1.0	3.670000e+03		1.0	Zhejiang	False
Fujian OCGT-2010.0	Fujian gas	Fujian OCGT		gas OCGT	0.4	True	2010	25.0	9750.0	0.0	...	0	NaN	NaN	1.0	1.0	9.750000e+03		1.0	Fujian	False
Guangdong OCGT-2010.0	Guangdong gas	Guangdong OCGT		gas OCGT	0.4	True	2010	25.0	8250.0	0.0	...	0	NaN	NaN	1.0	1.0	8.250000e+03		1.0	Guangdong	False
Henan OCGT-2010.0	Henan gas	Henan OCGT		gas OCGT	0.4	True	2010	25.0	3900.0	0.0	...	0	NaN	NaN	1.0	1.0	3.900000e+03		1.0	Henan	False
InnerMongolia OCGT-2010.0	InnerMongolia gas	InnerMongolia OCGT		gas OCGT	0.4	True	2010	25.0	750.0	0.0	...	0	NaN	NaN	1.0	1.0	7.500000e+02		1.0	InnerMongolia	False
Jiangsu OCGT-2010.0	Jiangsu gas	Jiangsu OCGT		gas OCGT	0.4	True	2010	25.0	975.0	0.0	...	0	NaN	NaN	1.0	1.0	9.750000e+02		1.0	Jiangsu	False
Shanghai OCGT-2010.0	Shanghai gas	Shanghai OCGT		gas OCGT	0.4	True	2010	25.0	3000.0	0.0	...	0	NaN	NaN	1.0	1.0	3.000000e+03		1.0	Shanghai	False
Zhejiang OCGT-2010.0	Zhejiang gas	Zhejiang OCGT		gas OCGT	0.4	True	2010	25.0	6392.5	0.0	...	0	NaN	NaN	1.0	1.0	6.392500e+03		1.0	Zhejiang	False
Guangdong OCGT-2015.0	Guangdong gas	Guangdong OCGT		gas OCGT	0.4	True	2015	25.0	1260.0	0.0	...	0	NaN	NaN	1.0	1.0	1.260000e+03		1.0	Guangdong	False
Jiangsu OCGT-2015.0	Jiangsu gas	Jiangsu OCGT		gas OCGT	0.4	True	2015	25.0	2375.0	0.0	...	0	NaN	NaN	1.0	1.0	2.375000e+03		1.0	Jiangsu	False
Shanghai OCGT-2015.0	Shanghai gas	Shanghai OCGT		gas OCGT	0.4	True	2015	25.0	5000.0	0.0	...	0	NaN	NaN	1.0	1.0	5.000000e+03		1.0	Shanghai	False
Sichuan OCGT-2015.0	Sichuan gas	Sichuan OCGT		gas OCGT	0.4	True	2015	25.0	1750.0	0.0	...	0	NaN	NaN	1.0	1.0	1.750000e+03		1.0	Sichuan	False
Tibet OCGT-2015.0	Tibet gas	Tibet OCGT		gas OCGT	0.4	True	2015	25.0	450.0	0.0	...	0	NaN	NaN	1.0	1.0	4.500000e+02		1.0	Tibet	False
Hainan OCGT-2020.0	Hainan gas	Hainan OCGT		gas OCGT	0.4	True	2020	25.0	2300.0	0.0	...	0	NaN	NaN	1.0	1.0	2.300000e+03		1.0	Hainan	False
Jiangsu OCGT-2020.0	Jiangsu gas	Jiangsu OCGT		gas OCGT	0.4	True	2020	25.0	2000.0	0.0	...	0	NaN	NaN	1.0	1.0	2.000000e+03		1.0	Jiangsu	False
Shanghai OCGT-2020.0	Shanghai gas	Shanghai OCGT		gas OCGT	0.4	True	2020	25.0	2121.0	0.0	...	0	NaN	NaN	1.0	1.0	2.121000e+03		1.0	Shanghai	False
Shanxi OCGT-2020.0	Shanxi gas	Shanxi OCGT		gas OCGT	0.4	True	2020	25.0	360.0	0.0	...	0	NaN	NaN	1.0	1.0	3.600000e+02		1.0	Shanxi	False

55 rows × 38 columns

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